Skip to main content

Advertisement

Springer Nature Link
Log in

Using an explicit query and a topic model for scientific article recommendation

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

The search for relevant scientific articles is a crucial step in any research project. However, the vast number of articles published and available online in digital databases (Google Scholar, Semantic Scholar, etc.) can make this task tedious and negatively impact a researcher's productivity. This article proposes a new method of recommending scientific articles that takes advantage of content-based filtering. The challenge is to target relevant information that meets a researcher's needs, regardless of their research domain. Our recommendation method is based on semantic exploration using latent factors. Our goal is to achieve an optimal topic model that will serve as the basis for the recommendation process. Our experiences confirm our performance expectations, showing relevance and objectivity in the results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Data availability

The data that support the findings of this study are openly available in “Elsevier OA CC-By Corpus” at http://doi.org/10.17632/ZM33CDNDXS.

References

  • Adomavicius, G. & Tuzhilin, A. (2005). Toward the next generation of recommender systems : a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6). https://doi.org/10.1109/TKDE.2005.99

  • Albalawi, R., Yeap, T. H. & Benyoucef, M. (2020). Using Topic Modeling Methods for Short-Text Data : A Comparative Analysis. Frontiers in artificial intelligence, 3. https://doi.org/10.3389/frai.2020.00042

  • Almonte, L., Guerra, E., Cantador, I., & de Lara, J. (2022). Recommender systems in model-driven engineering. Software and Systems Modeling, 21, 249–280. https://doi.org/10.1007/s10270-021-00905-x

    Article  Google Scholar 

  • Amami, M., Pasi, G., Stella, F., & Faiz, R. (2016). An LDA-based approach to scientific paper recommendation. In Springer eBooks (pp. 200–210). Springer Nature. https://doi.org/10.1007/978-3-319-41754-7_17

  • Bagul, D., & Barve, S. (2021). A novel content-based recommendation approach based on LDA topic modeling for literature recommendation. International Conference on Inventive Computation Technologies. https://doi.org/10.1109/icict50816.2021.9358561

    Article  Google Scholar 

  • Bai, X., Wang, M., Lee, I., Yang, Z., Kong, X., & Xai, F. (2020). Scientific Paper Recommendation : A Survey. IEEE Access, 7, 9324–9339.

    Article  Google Scholar 

  • Beel, J., Gipp, B., & Langer, S. (2016). Research-paper recommender systems : A literature survey. International Journal on Digital Libraries, 17, 305–338. https://doi.org/10.1007/s00799-015-0156-0

    Article  Google Scholar 

  • Berhil, S., Benlahmar, H., & Labani, N. (2020). A review paper on artificial intelligence at the service of human resources management. Indonesian Journal of Electrical Engineering and Computer Science, 18, 32–40.

    Article  Google Scholar 

  • Blei, D., & M., Ng, A. & Jordan, M., I. (2000). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.

    MATH  Google Scholar 

  • Boussaadi, S., Aliane, H., Abdeldjalil, O., Houari, D., & Djoumagh, M. (2020). Recommender systems based on detection community in academic social network. 2020 International Multi-Conference on : “Organization of Knowledge and Advanced Technologies” (OCTA). https://doi.org/10.1109/octa49274.2020.9151729

  • Guo, C., Lu, M., & Wei, W. (2021). An Improved LDA Topic Modeling Method Based on Partition for Medium and Long Texts. Annals of Data Science, 8, 331–344. https://doi.org/10.1007/s40745-019-00218-3

    Article  Google Scholar 

  • Hadhiatma, A., Azhari, A., & Suyanto, Y. (2023). A Scientific Paper Recommendation Framework Based on Multi-Topic Communities and Modified PageRank. IEEE Access., 1–1. https://doi.org/10.1109/ACCESS.2023.3251189

  • Hong, K., Jeon, H. & Jeon, C. (2013). Personalized Research Paper Recommendation System using Keyword Extraction Based on UserProfile. Journal of Convergence Information Technology, 106–116

  • Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y., & Zhao, L. (2019). Latent Dirichlet allocation (LDA) and topic modeling : Models, applications, a survey. Multimedia Tools and Applications, 78(11), 15169–15211. https://doi.org/10.1007/s11042-018-6894-4

    Article  Google Scholar 

  • Jooneghani, Z. K. N. (2021). Comparison of topic modeling methods for analyzing tweets on Covid-19 vaccine.

  • Kershaw, D., & Koeling, R. (2020). Elsevier OA CC-BY corpus. Elsevier Data Repository, V3. https://doi.org/10.17632/zm33cdndxs.3

  • Lee, D. & Seung, H. (1998). Learning the parts of objects by non-negative matrix factorization. Nature, 788–791. https://doi.org/10.1038/44565

  • Leung, Y. T., & Khalvati, F. (2022). Exploring COVID-19–Related stressors : Topic modeling study. Journal of Medical Internet Research, 24(7), e37142. https://doi.org/10.2196/37142

    Article  Google Scholar 

  • Levene, M. (2022). A Skew Logistic Distribution for Modelling COVID-19 Waves and Its Evaluation Using the Empirical Survival Jensen-Shannon Divergence. Entropy, 24(5), 600. https://doi.org/10.48550/arXiv.2201.13257

    Article  MathSciNet  Google Scholar 

  • Rossetti, M., Pettit, B., Vargas, S., Kershaw, D., Jack, K., Magatti, D. & Hristakeva, M. (2017). Effectively identifying users’ research interests for scholarly reference management and discovery. the 1st Workshop on Scholarly Web Mining, 17–24. https://doi.org/10.1145/3057148.3057151

  • Sakib, N., Ahmad, R., B. & Haruna, K. (2022). A collaborative approach toward scientific paper recommendation using citation context. IEEE Access, 8, 51246–51255.

  • Stevens, K., Kegelmeyer, P., Andrzejewski, D. & Buttler, D. (2012). Exploring topic coherence over many models and many topics. conference on empirical methods in natural language processing and computational natural language learning, 952–961

  • Subathra, P., & Kumar, P. (2019). Recommending Research Article Based on User Queries Using Latent Dirichlet Allocation. Springer Singapore eBooks, 163–175. https://doi.org/10.1007/978-981-15-2475-2_15

  • Wang, C. & Blei, D., M. (2011). Collaborative topic modeling for recommending scientific articles. Knowledge discovery and data mining, 448–456

Download references

Acknowledgements

Not applicable.

Funding

No funding was received for conducting this study.

Author information

Authors and Affiliations

  1. DTISI, Research Center on Scientific and Technical Information Cerist, Algiers, Algeria

    Boussaadi Smail

  2. Director of Information Sciences R&D Laboratory Head of Natural Language Processing and Digital Content Team Cerist, Algiers, Algeria

    Hassina Aliane

  3. 1.Polytech Tours, Imaging anBrain, University of Tours, INSERM U930, Tours, France

    Ouahabi Abdeldjalil

Authors
  1. Boussaadi Smail
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Hassina Aliane
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Ouahabi Abdeldjalil
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Boussaadi Smail.

Ethics declarations

Conflict of interest

None.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Smail, B., Aliane, H. & Abdeldjalil, O. Using an explicit query and a topic model for scientific article recommendation. Educ Inf Technol 28, 15657–15670 (2023). https://doi.org/10.1007/s10639-023-11817-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-023-11817-2

Keywords